Abstract

CP-nets offer a compact qualitative representation of human preferences that operate under ceteris paribus ("with all else being equal") semantics. In this paper we present a novel algorithm through which an agent learns the preferences of a user. CP-nets are used to represent such preferences and are learned online through a series of queries generated by the algorithm. Our algorithm builds a CP-net for the user by creating nodes and initializing CPTs, then gradually adding edges and forming more complex CPTs consistent with responses to queries until a confidence parameter is reached.